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(import 'java.lang.management.ManagementFactory)
(import 'com.sun.management.HotSpotDiagnosticMXBean)

(let [server (ManagementFactory/getPlatformMBeanServer)
      bean-name "com.sun.management:type=HotSpotDiagnostic"
      bean (ManagementFactory/newPlatformMXBeanProxy server bean-name HotSpotDiagnosticMXBean)
      live-objects-only? false]
  (.dumpHeap bean "dump.hprof" live-objects-only?))

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"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

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(ns datastructures.queue
  (:refer-clojure :exclude [compare-and-set! pop])
  (:import java.util.concurrent.atomic.AtomicReference))

(defn- compare-and-set! [^AtomicReference aref oldval newval]
  (or (.compareAndSet aref oldval newval)
      (throw (RuntimeException. ::loop))))

(defmacro with-contention
  [& body]

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(ns your.namespace.async-util
    (:require
      [clojure.core.async :as a :refer [>! <! >!! <!! go chan buffer
                                        close! thread alts! alts!! timeout]]
      [clojure.core.match :as match :refer [match]]))

;;
;; core.async util fns
;;

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Install Tensorflow-GPU (for NVIDIA GPUs) for use in JupyterLab using Anaconda

This tutorial is for computers with NVIDIA GPUs installed.

Tensorflow for GPU significantly reduces the time taken by Deep Neural Networks (like CNNs, LSTMs, etc) to complete each Epoch (compute cycle) by utilizing the CUDA cores present in the GPU for parallel processing.

The following steps are to be followed:

1. Make sure that you have installed the latest drivers of your NVIDIA GPU for your OS.

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const MAX_TIMESTEPS = 1000;

function initializeCartPoleEnvironment() {
  const gravity = 9.8; // Acceleration due to gravity, m/s^2
  const cartMass = 1.0; // Mass of the cart
  const poleMass = 0.1; // Mass of the pole
  const totalMass = cartMass + poleMass;
  const length = 0.5; // Half the pole's length
  const poleMassLength = poleMass * length;
  const forceMagnitude = 10.0; // Magnitude of the force applied to the cart

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const createFitnessFunction = (dataset, { repeat = 1, cost }) => {
  return network => {
    let score = 0
    for (let i = 0; i < repeat; i++) {
      const result = network.test(dataset, cost)

      if (!result || typeof result.error === 'undefined') {
        throw new Error('The test method did not return a valid result with an error property.')
      }

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function getCombinedConnections(n1conns, n2conns, score1, score2, equal) {
  // Split common conn genes from disjoint or excess conn genes

  const keys1 = Object.keys(n1conns)
  const keys2 = Object.keys(n2conns)
  const commonKeys = keys1.filter(key => n2conns[key] !== undefined)
  const commonConns = commonKeys.map(key => n1conns[key])

  const other1Keys = keys1.filter(key => commonKeys.includes(key) === false)
  const other2Keys = keys2.filter(key => commonKeys.includes(key) === false)

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class Node {
    constructor(t, isLeaf = true) {
        this.t = t; // Minimum degree
        this.keys = [];
        this.children = [];
        this.isLeaf = isLeaf;
    }

    // A utility function to insert a new key in the subtree rooted with this node.
    insertNonFull(k) {